Method for two time-scales video stream transmission control

ABSTRACT

A method for two time-scales video stream transmission control is proposed, which mainly includes the following three steps. Firstly, a model-based long time-scale bandwidth trend extraction step is proposed to calculate a current bandwidth through a Transmission Control Protocol (TCP) throughput model, and predict a trend of network bandwidth by using an Exponential Smooth Model (ESM) according to the calculated current bandwidth. Secondly, a short time-scale bandwidth fluctuation detection step is proposed to divide a network status into three categories and according to different network statuses, additively increase or multiplicatively decrease the estimated bandwidth. Thirdly, a target bit rate adjustment step based on two asymmetrical thresholds is proposed to set an up-threshold and a down-threshold of bandwidth to avoid frequently adjusting a target bit rate of an encoder. The method can satisfy the requirements of TCP-friendliness, real-time, and smoothness for the video transmission.

FIELD OF THE INVENTION

The present invention relates to a video stream transmission controlmethod, and more particularly to a method for two time-scales videostream transmission control, which belongs to the field of themultimedia communication technology.

BACKGROUND OF THE INVENTION

The real-time video transmission usually adopts theconnectionless-oriented User Datagram Protocol (UDP) due to its largedata volume and real-time requirement. However, there is no congestioncontrol mechanism in UDP. Currently, the internet is a Best-Effortnetwork and cannot ensure the Quality of Service (QoS), which results inthe problems such as the bandwidth fluctuations, the packet loss and thetransmission delay. Also, the network status changes dynamically andcannot be accurately estimated through the conventional models, which isa major problem for real-time video transmission. Therefore, to ensurethe QoS of the real-time video transmission, the adaptive videotransmission control mechanism has to be adopted, and the videotransmission control has the following three requirements:

(1) Transmission Control Protocol (TCP)-Friendliness: A flow isTCP-friendly if, and only if, in a steady state, it uses in the longterm no more bandwidth than a conforming TCP flow that would be usedunder comparable conditions, that is, a TCP-Friendly flow and a TCP flowcan evenly share the bandwidth for a long time in the same channel andthere is no aggressive occupation of the bandwidth.

(2) Real-time: To reduce the influence (on the video quality) frombandwidth fluctuations, which may be caused by abrupt changes ofbackground traffic, the real-time transmission control has to detect thenetwork bandwidth fluctuations, and correspondingly, to adjust thesending rate to adapt to the network bandwidth fluctuations in realtime, so as to reduce the packet loss ratio during the videotransmission.

(3) Smoothness: The target bit rate of the encoder is determinedaccording to the bandwidth output by the real-time video transmissioncontrol, and furthermore, the target bit rate determines the perceptualquality of a video stream. The perceptual quality of a video stream isbetter in the case of a slightly degradation of image fidelity butmaintaining a smooth subjective quality than in the case of a highfidelity but a serious fluctuation of subjective quality. Therefore,smoothness is a special requirement of the video transmission control.

The real-time video transmission needs to meet different requirementswithin different time-scales, namely, a long time-scale requirement anda short time-scale requirement. The real-time video transmission has anobvious characteristic of two time-scales in terms of both the networkbandwidth fluctuation and the perceptual quality of video stream.

The network bandwidth can be regarded as a time sequence and can bedivided into two parts, namely, the trend item and the disturbance item,as shown in FIG. 1. The trend item is a general tendency of the networkbandwidth and shows the direction in the way network bandwidth isdeveloping, which is usually relatively smooth. Therefore, the trend ofnetwork bandwidth can be appropriately predicted through a suitableprediction model. The trend item is the long time-scale feature of thenetwork bandwidth, and the TCP-friendly requirement of videotransmission control reflects such the long time-scale requirement(according to the aforementioned definition of TCP-friendliness). Due tothe changes of the network background traffic and the instability of thevideo traffic (caused by the fluctuations of the video quality), theactual network bandwidth fluctuates around the bandwidth trend within ashort time. The variation of the network bandwidth within a short timeis regarded as a disturbance item and is mainly caused by an abruptchange of the network background traffic. The disturbance item is theshort time-scale feature of the network bandwidth, and the real-timerequirement of the video transmission control reflects such the shorttime-scale requirement.

The real-time video transmission requires that the network bandwidth canbe friendly shared in a long time-scale and the fluctuations of thenetwork bandwidth can be quickly responded in a short time-scale.Therefore, to ensure the quality of the real-time video transmission, amultiple-scales video stream transmission control mechanism is required.In the long time-scale, the trend of the network bandwidth should beaccurately extracted, and meanwhile, the TCP-friendliness of thetransmission should be ensured. On the other hand, in the shorttime-scale, the fluctuations of the network bandwidth should beresponded in time, and meanwhile, the smoothness of video quality shouldbe ensured. According to the above analysis, the real-time videotransmission control has an obvious multiple time-scales requirement.Therefore, in order to meet the requirements of TCP-friendliness,real-time and smoothness, the video transmission control scheme shouldbe designed aiming at different time-scales.

The conventional transmission control methods can be divided into twocategories, namely, the model-based method and the additiveincrease/multiplicative decrease (AIMD) method. In the model-basedmethod, the TCP throughput model is employed to calculate the networkbandwidth according to network feedback information, such as the packetloss ratio, transmission delay. This method can achieve a smoothbandwidth estimation and can efficiently extract the trend of networkbandwidth. However, the TCP throughput model highly relates to thenetwork feedback information, which may result in some delay, such as,the statistics and the transmission of feedback information at thedecoder (video receiver), and the processing of the feedback informationat the encoder (video sender). So there will be a mismatch between theestimated bandwidth and the current actual bandwidth. Therefore, themodel-based method cannot adapt to the bandwidth fluctuation in time. Inthe AIMD method, according to different network statuses, the previousestimated bandwidth is additively increased or multiplicativelydecreased to achieve the estimation of current actual bandwidth. So theAIMD method can quickly adapt to the network bandwidth fluctuations. Butbecause the output bandwidth of AIMD method will result in a sawtoothshape similar to that of TCP within a short time, the AIMD method cannotachieve the smooth estimation of bandwidth. Moreover, both the above twomethods are single time-scale control method, which cannot meet themultiple time-scales requirement of video transmission control.

SUMMARY OF THE INVENTION

The present invention provides a method for two time-scales video streamtransmission control. The method includes long time-scale control andshort time-scale control, so as to implement video stream transmissioncontrol of multiple time-scales and provide a stable and high qualityvideo stream to users.

To achieve the above objective, the present invention adopts thefollowing technical solution.

A method for two time-scales video stream transmission control, whichincludes the following steps:

(A) dividing transmission control into two different time-scales,namely, a long time-scale and a short time-scale, and one longtime-scale includes a plurality of short time-scales;

(B) for the long time-scale transmission control, calculating thecurrent bandwidth through the TCP throughput model, predicting the trendof network bandwidth by using an Exponential Smooth Model (ESM)according to the calculated current bandwidth, and using the predictedtrend of network bandwidth as an initial value for the subsequent shorttime-scale transmission control;

(C) for the short time-scale transmission control, according todifferent network transmission packet loss ratio, dividing the networkstatus into three categories, namely the light load, the full load, andthe congestion load; when the network status is the light load,increasing the estimated bandwidth through an additive factor; when thenetwork status is the congestion load, decreasing the estimatedbandwidth through a multiplicative factor; and when the network statusis full load, keeping the estimated bandwidth unchanged;

(D) during the adjustment of the encoder target bit rate, setting anup-threshold and a down-threshold for the estimated bandwidth; in whichif the difference between the estimated bandwidth and the current targetbit rate of the encoder is between the up-threshold and thedown-threshold, the encoder maintain the current target bit rate; andotherwise, the encoder adjusts the target bit rate according to theestimated bandwidth.

For the method for two time-scales video stream transmission control asdiscussed above, the feature is that in Step (B), the ESM in followingEquation (1) is adopted to predict the trend of network bandwidth:T _(t) =α×x _(t)+(1−α)×T _(t-1)  (1)where T_(i) is the predicted trend of bandwidth at time t, x_(t) is thecalculated bandwidth at time t through the TCP throughput model, α isthe smoothness factor and its range is (0,1).

In the above method for two time-scales video stream transmissioncontrol, the feature lies in that the smoothness factor α is dynamicallyadjusted according to the current bandwidth statues. When the networkbandwidth increases, α equals to a relatively small value within therange of (0,1). When the network bandwidth decreases, α equals to arelatively big value within the range of (0,1).

In the above method for two time-scales video stream transmissioncontrol, the feature lies in that in Step (C) the following Equation (2)is adopted to decide the multiplicative factor which is used to decreasethe estimated bandwidth:ω=1−β·√{square root over (P _(loss)(n))}  (2)where ω is the multiplicative factor, P_(loss)(n) is the packet lossratio at time n, β is a coefficient, and βε[0,1].

In the above method for two time-scales video stream transmissioncontrol, the feature lies in that in Step (C) the decision of theadditive factor specifically includes:

if no congestion has occurred within a long time-scale, setting theadditive factor as a constant;

if congestion has occurred within a long time-scale, the followingEquation (3) is adopted to decide the additive factor:θ=(1−e ^(−0.5(1-f(n)/T) ^(t) ⁾)(F−f(n))  (3)where θ is the additive factor, F is the estimated bandwidth when thelast congestion occurs, T_(t) is the predicted trend of networkbandwidth output by the long time-scale transmission control, and f(n)is the estimated bandwidth at time n.

In the above method for two time-scales video stream transmissioncontrol, the feature lies in that in Step (D), the values of theup-threshold and the down-threshold change according to differentnetwork bandwidth ranges, their values are asymmetrical, and theup-threshold is greater than the down-threshold.

In the present invention, a two time-scales video transmission controlmethod is proposed to include the long time-scale transmission controland the short time-scale transmission control. By combining thetransmission control with the encoding control, the proposed method canensure the requirements of TCP-Friendliness, real-time and smoothness.The present invention can desirably meet the multiple time-scalesrequirement of real-time video transmission, and meanwhile, caneffectively utilize the network bandwidth resources to provide a stableand high quality video stream.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure will become more fully understood from thedetailed description given herein below for illustration only, and thusare not limitative of the present disclosure, and wherein:

FIG. 1 is a schematic exploded diagram of a bandwidth;

FIG. 2 is a schematic block diagram of a method for two time-scalesvideo stream transmission control;

FIG. 3 is a comparison diagram between the modified ESM model and thetraditional ESM model;

FIG. 4 is a curve diagram of recovery quality of video sequences indifferent packet loss ratios;

FIG. 5 is a flow chart of additive factor adjustment;

FIG. 6 is a schematic diagram of an effect of additive factor dynamicadjustment;

FIG. 7 is a flow chart of a method for detecting bandwidth fluctuationsbased on short time-scale of three statuses; and

FIG. 8 is a schematic diagram of an effect of bandwidth detection andadjustment.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The present invention provides a method for two time-scales video streamtransmission control and the specific implementation steps are as shownin FIG. 2, which mainly include the following three parts. Firstly, amodel-based long time-scale bandwidth trend extraction method isproposed to calculate the current bandwidth through the TransmissionControl Protocol (TCP) throughput model, and predict the trend ofnetwork bandwidth by using an Exponential Smooth Model (ESM) accordingto the calculated current bandwidth. Secondly, a short time-scalebandwidth fluctuation detection method is proposed to divide the networkstatus into three categories, namely, the light load, the full load andthe congestion load, and according to different network statuses,additively increase or multiplicatively decrease the estimated bandwidththrough the proposed additive factor or multiplicative factor. Thirdly,a target bit rate adjustment method based on two asymmetrical thresholdsis proposed to set an up-threshold and a down-threshold of bandwidth toavoid frequently adjusting the target bit rate of the encoder and toensure the smooth perceptual quality. The present invention has adistinct characteristic of two time-scales and can ensure therequirements of TCP-friendliness, real-time, and smoothness for thevideo transmission. The division of the long time-scale and the shorttime-scale may be decided according to different applicationrequirements, for example, the long time-scale can be set to 2 minutesand the short time-scale can be set to 2 seconds. The specificillustration is provided below.

1 The Model-Based Long Time-Scale Bandwidth Trend Extraction Method

In order to efficiently extract the trend of network bandwidth, arelatively long measurement interval is required. The AIMD method issuitable for responding to the fluctuation of network bandwidth within ashort time-scale but not suitable for the measurement within a longtime-scale. In the aforementioned model-based transmission controlmethod, the trend of network bandwidth can be extracted by extending themeasurement interval. In addition, the negative impact of backgroundtraffic changes within a short time can be reduced and the feedbackinformation, such as the packet loss ratio and round-trip delay, can beaccurately measured by extending the measurement intervals. Therefore,the model-based transmission control method is more suitable for theextraction of the network bandwidth trend.

However, the extension of measurement interval will incur the lagbetween the current actual bandwidth and the trend of the bandwidth.Therefore, it is necessary to employ a appropriate prediction method andutilize the historical feedback information together with the calculatedbandwidth (through the TCP throughput model) to predict the trend of thebandwidth. In the implementation, a common ESM may be adopted for thebandwidth trend prediction. It can be understood that other predictionmethods (for example, the moving average and the weighted movingaverage) can also be adopted.

The ESM is a weighted prediction model, which requires the currentmeasurement value, the previous prediction value and the smoothnessfactor for prediction, and has very low data storage and computationalcomplexity. Moreover, the data output through the ESM can maintain adesirable smoothness. The equation of the ESM is shown as follows:T _(t) =α×x _(t)+(1−α)×T _(t-1)  (1)where T_(t) is the bandwidth trend prediction at time t, T_(t-1) is thebandwidth trend prediction at time t−1, α is the smoothness factor, andits range is (0,1), and x_(t) is the bandwidth value calculated throughthe TCP throughput model, and the TCP throughput model is as shown inEquation (2):

$\begin{matrix}{x_{t} = {\min\left\{ {\frac{w_{m^{s}}}{t_{RTT}},\frac{s}{{t_{RTT}\sqrt{\frac{2{bp}}{3}}} + {t_{RTO}{\min\left( {1,\sqrt{\frac{3{bp}}{8}}} \right)}{p\left( {1 + {32p^{2}}} \right)}}}} \right\}}} & (2)\end{matrix}$where t_(RTT) is the network transmission round-trip delay, t_(RTO) isthe retransmission time-out, s is the size of a data packet, p is thepacket loss ratio, W_(m) is the largest congestion window, b is thenumber of data packets that are successfully received as can beconfirmed by a ACK (Acknowledge) feedback packet, usually, b is equal to1.

Generally, the ESM model adopts a constant smoothness factor α toimplement the smooth prediction. Another optimal method is todynamically determine the smoothness factor according to differentnetwork statuses, namely increase or decrease, so as to make theadjustment possess the basic characteristic of “additiveincrease/multiplicative decrease”, and so as to further ensure theTCP-friendliness.

In this embodiment, the transmission control is divided into twodifferent time-scales, namely, a long time-scale and a short time-scale.One long time-scale includes a plurality of short time-scales. Theprocessing flow of the optimal model-based long time-scale bandwidthtrend extraction method in this embodiment is as follows.

S1: An encoder (video sender) receives feedback information such as anetwork packet loss ratio p and a round-trip delay t_(RTT) andcalculates the bandwidth through the TCP throughput model (Equation(2)).

S2: According to the calculated bandwidth in S1 and a group of previouscalculated bandwidth, the network status is determined. If the networkstatus is determined only according to a single previous calculatedbandwidth, the adjustment will become too frequent. Therefore, in orderto accurately determine a network status and avoid short-termdisturbances, the weighted average X of the group of previous calculatedbandwidth is taken as the threshold for determining the network status.When the calculated bandwidth is bigger than the weighted average(x_(i)≧ X), the bandwidth trend is regarded as increasing, and when thecalculated bandwidth is smaller than the weighted average (x_(i)< X),the bandwidth trend is regarded as decreasing.

S3: According to different network statuses, the smoothness factor α isdynamically determined by considering the fluctuations of the networkbandwidth. The ESM model is adopted to predict the trend of networkbandwidth with the calculated bandwidth and the modified smoothnessfactor α.

In the ESM model, the prediction value T_(t) is a corrected valueacquired by adding the previous prediction value T_(t-1) to theamendment x_(t)−T_(t-1) (produced in the previous prediction). Thesmoothness factor α determines a correction extent, when the value of αbecomes larger, the correction extent becomes larger. The intensity ofthe network fluctuations needs to be considered during the determinationof the smoothness factor. According to different network statuses, thesmoothness factors should be determined to adapt to the basiccharacteristic of the TCP congestion control, namely “additiveincrease/multiplicative decrease”. That is, when the network bandwidthdecreases, a relative large smoothness factor is utilized to quicklyadapt to the trend of network bandwidth. When the network bandwidthincreases, a relative small smoothness factor is utilized to ensure asmooth bandwidth increase. Additionally, when the network bandwidthfluctuates seriously, a relative large smoothness factor is utilized toquickly adapt to the change of the network bandwidth. When the networkbandwidth changes smoothly, a relative small smoothness factor isutilized to maintain the smoothness.

In this embodiment, the proposed optimal method for selecting thesmoothness factor considers the above two aspects and to define thesmoothness factor as a function of the calculated bandwidth x, and theweighted average X. The smoothness factor is defined as in the followingequation.

$\begin{matrix}{\alpha = \left\{ {{\begin{matrix}{\min\left( {1,\left( {{\beta_{1}\frac{\max\left( {x_{i},\overset{\_}{X}} \right)}{\min\left( {x_{i},\overset{\_}{X}} \right)}} + \lambda} \right)} \right)} & {x_{i} < \overset{\_}{X}} \\{\min\left( {1,\left( {{\beta_{2}\frac{\max\left( {x_{i},\overset{\_}{X}} \right)}{\min\left( {x_{i},\overset{\_}{X}} \right)}} + \lambda} \right)} \right)} & {x_{i} \geq \overset{\_}{X}}\end{matrix}\lambda} = \sqrt{\frac{1}{N}{\sum\limits_{j = 1}^{N}\left( {\left( {x_{j} - \overset{\_}{X}} \right)/\left( {x_{j} + \overset{\_}{X}} \right)} \right)^{2}}}} \right.} & (3)\end{matrix}$where β₁ and β₂ are two constants within (0,1), and β₁>β₂. It is notedthat max(x_(i), X)/min(x_(i), X)≧1, when x_(i)≧ X, a small constant β₂may decrease the smoothness factor α. On the other side, when x_(i)< X,that is, the network bandwidth decreases, the large constant β₁ mayincrease the smoothness factor α. At the same time, the value of λ ischanged according to different fluctuation degrees of the bandwidth.That is, when the bandwidth fluctuates seriously, λ equals to arelatively bigger value, and vice versa.

The modified ESM model adopts the smoothness factor α to predict thetrend of network bandwidth. The comparison between the modified ESMmodel (MESM, in which N is 20, β₁ is 0.2, and β₂ is 0.1) and thetraditional ESM model (TESM) is as shown in FIG. 3. The smoothnessfactor α in FIG. 3A is 0.1, the smoothness factor α in FIG. 3B is 0.4,and the smoothness factor α in FIG. 3C is 0.7. As shown in FIG. 3A, inthe TESM model, when the smoothness factor is a small value, theincrease of the bandwidth has a desirable smooth characteristic, but thedecrease of the bandwidth is still smooth, so the characteristic of“additive increase/multiplicative decrease” is not met. In FIG. 3C, thesmoothness factor has a large value, the decrease of the bandwidth meetsthe change trend of the bandwidth well, but the increase thereof stillkeeps this characteristic, so such adjustment is sensitive to the changeof the bandwidth, causing that the adjustment is not smooth, andmeanwhile, the characteristic of “additive increase/multiplicativedecrease” is also not met. The smoothness factor in FIG. 3B has a mediumvalue, the decrease speed is similar, to the MESM but the increase speedis still too high. Meanwhile, as the smoothness factor in FIG. 3B has afixed value, for different situations of network fluctuations, theadjustment mode is the same, and the influences of the bandwidthdisturbance are inevitable, and the smoothness of the output valuecannot be ensured. In general, the modified ESM model, while achievingan accurate prediction, (a relative accumulated prediction error is5.77%, and the number of prediction points that a relative predictionerror is smaller than 10% occupies 81.70% of the total number ofprediction points), has a desirable “additive increase/multiplicativedecrease” characteristic, and meanwhile, can adaptively adjust thesmoothness factor according to different degrees of the bandwidthfluctuations, so the overall control result has desirable smoothness.Therefore, the present invention may adopt the TESM model but preferablyadopt the MESM model.

The bandwidth trend prediction obtained through the model-based longtime-scale bandwidth trend extraction method is used as an initial valuefor bandwidth fluctuation detection in a short time-scale.

2 A Short Time-Scale Bandwidth Fluctuation Detection Method Based onTriple-Statuses

A short measurement interval needs to be used for the bandwidthfluctuation detection. The additive increase/multiplicative decreasemethod (AIMD) is suitable for responding to network fluctuations withina short time and its basic idea is: additively increase the estimatedbandwidth when there is no congestion, multiplicatively decrease theestimated bandwidth when there is a network congestion, and maintain theestimated bandwidth when the network status is full load. The AIMDmethod can be described by the following equation:

$\begin{matrix}{{f\left( {n + 1} \right)} = \left\{ {\begin{matrix}{\omega \times {f(n)}} & {Congestion} \\{{f(n)} + \theta} & {Others}\end{matrix}\mspace{14mu}\left( {{0 \leq \omega < 1},{\theta \geq 0}} \right)} \right.} & (4)\end{matrix}$where the parameter ω is the multiplicative factor, the parameter θ isthe additive factor, f(n+1) and f(n) are estimated bandwidths at timen+1 and n, respectively. The AIMD method can efficiently avoidfrequently triggering the network congestion (caused by rapidly increaseof the estimated bandwidth), which is one of the advantages of the AIMDmethod. However, the additive factor for increasing the estimatedbandwidth is a constant and cannot dynamically adapt to differentnetwork bandwidth fluctuation. If the additive factor is set to be arelative small value, the bandwidth increases too slowly (such that thenetwork bandwidth resources cannot be fully utilized). If the additivefactor is set to be a relative large value, the change of the bandwidthbecomes too frequent. In addition, the multiplicative factor is also setto be a constant in advance. When the estimated bandwidth is reduced,the practical congestion level of the network is not fully considered.In addition, the output bandwidth of the AIMD method cannot maintainsmooth and may result in a sawtooth shape within a short time.

However, the video transmission can tolerate a relative small amount ofpacket loss. Our experimental results indicate that the quality of thereconstructed video at the decoder (video receiver) decreases with theincrease of the packet loss ratio. When the packet loss ratio is withina certain range, the quality of the reconstructed video can be ensured.When the packet loss ratio exceeds this certain range, the quality ofthe reconstructed video will be degraded seriously. Therefore, in thisapplication, the packet loss ratio is employed as a main index for thedivision of network channel status. It can be understood that othernetwork feedback parameters (such as round-trip delay jitters) can alsobe employed. To prevent the video quality at the decoder (videoreceiver) from serious fluctuation caused by frequently adjusting theencoding target bit rate, the network status is divided into threecategories, namely, the light load, the full load and the congestionload according to two predefined thresholds, P₁ and P₂. If the packetloss ratio P_(loss)(n) is smaller than the lower threshold P₁, theestimated bandwidth f(n) is additively increased. If the P_(loss)(n) isgreater than the upper threshold P₂, the estimated bandwidth f(n) ismultiplicatively decreased.

The selection of the two thresholds P₁ and P₂ needs to consider theinfluence thereof on the video quality, a relative small value should beassigned to the threshold to reduce the influences of the QoSoscillation. The threshold should be determined to ensure that the videoquality at the decoder is within a controllable range. FIG. 4 shows PeakSignal to Noise Ratio (PSNR) values of the reconstructed videos of thefive standard video standard sequences (namely, Foreman, Football, Bus,Boat and Piano) under different packet loss ratios. As shown in FIG. 4,when the packet loss ratio is between 0.03 and 0.07, the change of thePSNR value is not obvious. When the packet loss ratio exceeds 0.07, thePSNR value starts to obviously decrease. Therefore, the thresholds andcan be set to 0.03 and 0.07, respectively.

The above conventional method is applicable to the present application.More preferably, on the basis of dividing the network status, thepresent application further propose a bandwidth decision method ofdynamically adjusting the additive factor and the multiplicative factor,which is described as follows

$\begin{matrix}{{f\left( {n + 1} \right)} = \left\{ \begin{matrix}{\max\left\{ {{{f(n)} + \theta},F_{\min}} \right\}} & {{P_{loss}(n)} \leq P_{1}} & \; \\{\min\left\{ {{\omega \cdot {f(n)}},F_{\max}} \right\}} & {{P_{loss}(n)} > P_{2}} & \left( {{0 \leq \omega < 1},{\theta \geq 0}} \right) \\{f(n)} & {else} & \;\end{matrix} \right.} & (5)\end{matrix}$where the range of the estimated bandwidth f (n) is set to [F_(min),F_(max)], the predicted trend network bandwidth T_(t) is used as theinitial value, that is, f(0)=T_(t).

For the transmission system based on the RTCP protocol, since there issome delay of the collection and transmission of RTCP feedbackinformation, the adjustment according to the RTCP feedback may take along time to achieve a stable value. Therefore, during bandwidthadjustment, not only the current packet loss ratio and congestion levelof the network, but also the lag of the RTCP feedback information shouldbe considered. Therefore, the method for deciding the multiplicativefactor ω is to set the factor as a function of the packet loss ratio:ω=1−β·√{square root over (P _(loss)(n))}  (6)where ω is the multiplicative factor, the coefficient βε[0,1], with theincrease of β, the influence of the packet loss ratio on themultiplicative factor ω also increases, and vice versa.

In the dynamic adjustment of the additive factor, the current estimatedbandwidth is determined according to the previous estimated bandwidthf(n) and the estimated bandwidth when the last congestion occurs. Whenthe congestion occurs, the estimated bandwidth is multiplicativelydecreased and the estimated bandwidth at this time is recorded as F. Ifthe difference between f(n) and F is relatively large, the additivefactor equals to a constant R_(c). If the difference between f(n) and Fis relatively small but still not close enough to the estimatedbandwidth when the last congestion occurs, the additive factor isdetermined according to the difference between f(n) and F. The smallerthe difference is, the smaller the additive factor is. If the estimatedbandwidth is close to the bandwidth of the previous congestion but thenetwork status is the light load, it indicates that the previous networkcongestion occurs temporarily, and the additive factor equals to theconstant R_(c). The method for dynamically adjusting the additive factoris described as follows and the specific adjustment method is as shownin FIG. 5.

It is assumed that F is the estimated bandwidth when the last congestionoccurs within a long time-scale. T_(t) is the predicted trend of networkbandwidth output by the long time-scale transmission control. R_(c) is aconstant, and kε[0,1].

-   -   If no congestion has occurred within a long time-scale, setting        the additive factor as a constant, namely, θ=R_(c).    -   If congestion has occurred within a long time-scale and the        current network status is light load,        -   if F−f(n)<k·F, setting the additive factor as follows:            θ=(1−e ^(−0.5(1-f(n)/T) ^(t) ⁾)(F−f(n))  (7)        -   and otherwise, if F−f(n)≧k·F, setting the additive factor as            a constant, namely, θ=R_(c).

An example of dynamic adjustment of the additive factor is as shown inFIG. 6. It is assumed that the bandwidth when the last congestion occursis 1700 Kbps, the packet loss ratio is 0.2, the predicted trend ofnetwork bandwidth is 800 Kbps, β in the multiplication factordetermination is set to 0.5, and the fixed additive factor R_(c) is setto 30 Kbps. As shown in FIG. 6, compared with the method with the fixedadditive factor, the proposed method for dynamically adjusting theadditive factor can utilize the bandwidth more efficiently and canensure the smoothness.

In this embodiment, FIG. 7 shows a process of an optimal shorttime-scale bandwidth fluctuation detection method based ontriple-statuses.

3 A Target Bit Rate Smooth Adjustment Method Based on Two AsymmetricalThresholds

During the period of target bit rate adjustment, the encoder adjusts itstarget bit rate according to the current estimated bandwidth. Thetimelier that the encoding target bit rate is adjusted, the higher theutilization rate of the network bandwidth by the system is. However, thesmall change of bandwidth cannot cause apparent influences on thequality of reconstructed video in the decoder. Moreover, the frequentadjustment of the encoding target bit rate will cause frequent networkfluctuations.

In order to fully utilize the network bandwidth and to avoid networkfluctuations, the change of the PSNR of the reconstructed video isutilized as the criterion for adjusting the target bit rate of theencoder. The MPEG/VCEG experts increase the PSNR of the image by using0.5 dB as a unit. This is because the difference of 0.5 dB can bevisually perceived.

Therefore, in order to avoid apparent changes of the video qualityduring the adjustment of the target bit rate and in order to avoid thefrequent adjustment of the target bit rate of the encoder, anup-threshold th_(up) and a down-threshold th_(down) are respectively setin the process of turning up and turning down the target bit rate. Ifthe difference between the current estimated bandwidth and the previousestimated bandwidth is less than the up-threshold or less than thedown-threshold (i.e., Δth<th_(up) or Δth<th_(down)), the target bit rateof the encoder is not adjusted (Δth is acquired from the computationwith Equation 8). If the difference between the current estimatedbandwidth and the previous estimated bandwidth is not less than theup-threshold or not less than the down-threshold (i.e., Δth≧th_(up) orΔth≧th_(down)), the target bit rate of the encoder is adjusted to be thecurrent estimated bandwidth. The above method ensures that the networkbandwidth can be fully utilized and no apparent changes occur to thevideo quality during the adjustment of the target bit rate.Δth=|f(n)−f′|  (8)where f(n) is the estimated bandwidth at time n, and f′ is the estimatedbandwidth in the previous adjustment of the encoder.

According to different estimated bandwidth ranges, the selection of theadjustment thresholds th_(up) and th_(down) is different. Also, in orderto satisfy the basic characteristic of “additive increase/multiplicativedecrease”, these two thresholds are selected to be asymmetrical. When acongestion occurs on the network, the target bit rate of the encoderneeds to be turned down in time, and the sent data volume is decreased,so as to rapidly recover from the congestion. Therefore, the th_(down)is selected to be smaller than th_(up) for asymmetrical adjustment, sothat when the bandwidth is turned down by a small extent, the target bitrate of the encoder can also be turned down in time. For example, forthe video with the resolution of 352×288, through statistical analysisand the experiments of the standard test sequences and real-timecaptured videos, in this embodiment, the values of th_(up) and th_(down)are listed in Table 1.

Network Bandwidth Range th_(up) (kbps) th_(down) (kbps) >600 kbps 150 75128 kbps-600 kbps 30 15 <128 kbps 10 5

Table 1 Thresholds th_(up) and th_(down) for Different Ranges of NetworkBandwidth

The complete process of the method for two time-scales video streamtransmission control is as shown in FIG. 8. Firstly, the trend ofnetwork bandwidth is predicted by using the proposed model-based longtime-scale bandwidth trend extraction method to ensure the requirementof TCP-Friendliness of the video transmission in a long time-scale.Secondly, based on the predicted trend of network bandwidth, thebandwidth fluctuation is detected within a short time. In the proposedshort time-scale bandwidth fluctuation detection method, the predictedtrend of network bandwidth is used as the initial value, and accordingto the initial value and the feedback information of the network, thebandwidth fluctuations are dynamically detected, so as to ensure thereal-time requirement of the video transmission in a short time-scale.Thirdly, a target bit rate adjustment method based on two asymmetricalthresholds is proposed to set an up-threshold and a down-threshold ofbandwidth to avoid frequently adjusting the target bit rate of theencoder and to ensure a smooth perceptual quality.

Finally, it should be noted that the above embodiments are merelyprovided for describing the technical solutions of the presentinvention, but not intended to limit the present invention. It should beunderstood by persons of ordinary skill in the art that although thepresent invention has been described in detail with reference to theembodiments, modifications can be made to the technical solutionsdescribed in the embodiments, or equivalent replacements can be made tosome technical features in the technical solutions, as long as suchmodifications or replacements do not depart from the spirit and scope ofthe present invention.

What is claimed is:
 1. A method for two time-scales video streamtransmission control, wherein transmission control is divided into along time-scale and a short time-scale, one long time-scale includingseveral short time-scales, the method comprising the steps of: (A) inthe long time-scale transmission control, calculating a currentbandwidth through a Transmission Control Protocol (TCP) throughputmodel, and predicting a trend of network bandwidth by using anExponential Smooth Model (ESM) according to the calculated currentbandwidth; (B) in the short time-scale transmission control, using thetrend of network bandwidth predicted through Step (A) as an initialvalue, according to a network transmission packet loss ratio, dividing anetwork status into three categories, namely light load, full load, andcongestion, when the network status is the light load, increasing anestimated bandwidth through an additive factor; when the network statusis the congestion, decreasing the estimated bandwidth through amultiplicative factor; and when the network status is the full load,keeping the estimated bandwidth unchanged; and, the multiplicativefactor is decided by adopting the following equation:ω=1−β·√{square root over (P _(loss)(n))} where ω is the multiplicativefactor, P_(loss)(n) is a packet loss ratio at time n, β is acoefficient, and βε[0,1].
 2. A method for two time-scales video streamtransmission control, wherein transmission control is divided into along time-scale and a short time-scale, one long time-scale includingseveral short time-scales, the method comprising the steps of: (A) inthe long time-scale transmission control, calculating a currentbandwidth through a Transmission Control Protocol (TCP) throughputmodel, and predicting a trend of network bandwidth by using anExponential Smooth Model (ESM) according to the calculated currentbandwidth; (B) in the short time-scale transmission control, using thetrend of network bandwidth predicted through Step (A) as an initialvalue, according to a network transmission packet loss ratio, dividing anetwork status into three categories, namely light load, full load, andcongestion, when the network status is the light load, increasing anestimated bandwidth through an additive factor; when the network statusis the congestion, decreasing the estimated bandwidth through amultiplicative factor; and when the network status is the full load,keeping the estimated bandwidth unchanged; and, the decision of theadditive factor specifically comprises: if no congestion has occurredwithin a long time-scale, setting the additive factor as a constant; ifcongestion has occurred within a long time-scale, the following equationis adopted to decide the additive factor:θ=(1−e ^(−0.5(1-f(n)/T) ^(t) ⁾)(F−f(n)) where θ is the additive factor,F is an estimated bandwidth when the last congestion occurs, T_(t) is apredicted trend of network bandwidth output by the long time-scaletransmission control, and f(n) is an estimated bandwidth at time n. 3.The method for two time-scales video stream transmission controlaccording to claim 1 or 2, wherein in Step (A) the trend of networkbandwidth is predicted by adopting the ESM in the following equation:T _(t) =α×x _(t)+(1−α)×T _(t-1) where T_(t) is a trend of networkbandwidth at time t, x_(t) is a current bandwidth at time t calculatedthrough the TCP throughput model, and α is a smoothness factor, itsrange is (0,1).
 4. The method for two time-scales video streamtransmission control according to claim 3, wherein the smoothness factoris dynamically adjusted according to the current bandwidth status, whena network bandwidth increases, equals to a relatively small value withinthe range of, and when the network bandwidth decreases, equals to arelatively big value within the range of.
 5. The method for twotime-scales video stream transmission control according to claim 1 or 2,wherein further comprises Step (C): during adjustment of a target bitrate of an encoder, setting an up-threshold and a down-threshold for theestimated bandwidth, wherein if a difference between the estimatedbandwidth and a current target bit rate of the encoder is between thetwo thresholds, the target bit rate of the encoder is not adjusted; andotherwise, the encoder adjusts its target bit rate according to theestimated bandwidth.
 6. The method for two time-scale video streamtransmission control according to claim 5, wherein in Step (C) values ofthe up-threshold and the down-threshold change according to differentnetwork bandwidth ranges, their values are asymmetrical, and theup-threshold is greater than the down-threshold.